Using known nonself samples to improve negative selection algorithm

Li, Zhiyong; Li, Tao

doi:10.1007/s10489-021-02323-4

Cited by 6 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The compared algorithms include the real-valued negative selection algorithm (RVNSA) [28], improved negative selection algorithm (INSA) [29], known nonself (KN) [30], and adaptive immunoregulation negative selection algorithm (AI-NSA) [31]. It is assumed that 40% of the nonself set is known and used for INSA and KN to optimize the detector distribution.…”

Section: A Performance Comparison Of Up-to-date Nsas On a Benchmark D...mentioning

confidence: 99%

“…In NSAs, generating a large number of detectors is typically time-consuming because each generated detector must undergo self-tolerance and be checked against the existing detectors to remove redundancies. To verify the performance of the proposed algorithm, the same initial self-set is used with other algorithms, including RVNSA [28], INSA [29], KN [30], and AINSA [31]. The average increment of each round of candidate detectors is 100, and the maximum number of detectors is 58292, which is the total number of detectors generated by the proposed algorithm.…”

Section: ) Detector Generationmentioning

confidence: 99%

“…In addition, the detector movement is used to avoid a decreased coverage rate. Considering the deficiency of the random coverage of traditional NSAs, the known nonself is used as the candidate detector to further generate the detector and thereby repair holes [30]. In [31], an adaptive immunoregulation-based real-value NSA is proposed to calculate the self-radius and optimize the location of candidate detectors for different applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

Ye²

2023

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Considering a variety of sampled value (SV) attacks on busbar differential protection (BDP) which poses challenges to conventional learning algorithms, an algorithm to detect SV attacks based on the immune system of negative selection is developed in this paper. The healthy SV data of BDP are defined as self-data composed of spheres of the same size, whereas the SV attack data, i.e., the nonself data, are preserved in the nonself space covered by spherical detectors of different sizes. To avoid the confusion between busbar faults and SV attacks, a self-shape optimization algorithm is introduced, and the improved self-data are verified through a power-frequency fault-component-based differential protection criterion to avoid false negatives. Based on the difficulty of boundary coverage in traditional negative selection algorithms, a self-data-driven detector generation algorithm is proposed to enhance the detector coverage. A testbed of differential protection for a 110 kV double busbar system is then established. Typical SV attacks of BDP such as amplitude and current phase tampering, fault replays, and the disconnection of the secondary circuits of current transformers are considered, and the delays of differential relay operation caused by detection algorithms are investigated.

show abstract

Section: A Performance Comparison Of Up-to-date Nsas On a Benchmark D...mentioning

confidence: 99%

Section: ) Detector Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

Ye²

2023

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…To reduce the time complexity of the detector calculation, Yang et al [30] applied the antigen spatial density to calculate the low-dimensional subspace of densely aggregated antigens which generates detectors directly in these subspaces. Fouladvand et al [31] improved the real-valued negative selection algorithm based on Delaunay triangular dissection (dnyNSA) to generate detectors with more rational locations and sizes. Li et al [32] employed the known nonself as the candidate detector center to generate the detector and thus efectively improve the detection rate.…”

Section: Detector Matching Tolerancementioning

confidence: 99%

“…Li and Chen [33] used the Monte Carlo method to calculate the overlap volume of the hypersphere and proposed a nonself-covering calculation method based on confdence estimation. Fouladvand et al [31] compared the randomly generated pattern with the self-space GMM and retained the low probability random pattern as a detector. Yang et al [30] applied "antibody inhibition rate" instead of "expected coverage" as the termination condition.…”

Section: Hole Repairmentioning

confidence: 99%

A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly Detection

Yang

Wang

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Intrusion detection systems are crucial in fighting against various network attacks. By monitoring the network behavior in real time, possible attack attempts can be detected and acted upon. However, with the development of openness and flexibility of networks, artificial immunity-based network anomaly detection methods lack continuous adaptability and hence have poor detection performance. Thus, a novel framework for network anomaly detection with adaptive regulation is built in this paper. First, a heuristic dimensionality reduction algorithm based on unsupervised clustering is proposed. This algorithm uses the correlation between features to select the best subset. Then, a hybrid partitioning strategy is introduced in the negative selection algorithm (NSA), which divides the feature space into a grid based on the sample distribution density and generates specific candidate detectors in the boundary grid to effectively mitigate the holes caused by boundary diversity. Finally, the NSA is improved by self-set clustering and a novel gray wolf optimizer to achieve adaptive adjustment of the detector radius and position. The results show that the proposed NSA algorithm based on mixed hierarchical division and gray wolf optimization (MDGWO-NSA) achieves a higher detection rate, lower false alarm rate, and better generation quality than other network anomaly detection algorithms.

show abstract

Improved V-detector algorithm based on bagging for earthquake prediction with faults

Peng,

Liang,

Yang

2024

J Supercomput

View full text Add to dashboard Cite

Using known nonself samples to improve negative selection algorithm

Cited by 6 publications

References 30 publications

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

Sampled Value Attack Detection for Busbar Differential Protection Based on a Negative Selection Immune System

A Modified Gray Wolf Optimizer-Based Negative Selection Algorithm for Network Anomaly Detection

Improved V-detector algorithm based on bagging for earthquake prediction with faults

Contact Info

Product

Resources

About